Mining probabilistically frequent sequential patterns in uncertain databases

@inproceedings{Zhao2012MiningPF,
  title={Mining probabilistically frequent sequential patterns in uncertain databases},
  author={Zhou Zhao and Da Yan and Wilfred Ng},
  booktitle={EDBT},
  year={2012}
}
Data uncertainty is inherent in many real-world applications such as environmental surveillance and mobile tracking. As a result, mining sequential patterns from inaccurate data, such as sensor readings and GPS trajectories, is important for discovering hidden knowledge in such applications. Previous work uses expected support as the measurement of pattern frequentness, which has inherent weaknesses with respect to the underlying probability model, and is therefore ineffective for mining high… CONTINUE READING

Similar Papers

Loading similar papers…